Fuzzy-Based Detection of Injected False Data in Wireless Sensor Networks

Lee, Hae Young; Cho, Tae Ho; Kim, Hyung-Jong

doi:10.1007/978-3-642-13365-7_13

Cited by 5 publications

(5 citation statements)

References 11 publications

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“…Based on four factors, a fuzzy system determines FDIAs as well as the most appropriate countermeasures. Compared to the existing solutions [6], [7], the method can reduce the detection error and space complexity. Also, manual parameter settings are not required since the fuzzy system can be automatically optimized.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, it can be optimized by GAOM, so that the users need not to determine threshold parameters for the detection. Compared to FFD [6], the method can enhance the detection power slightly. Moreover, it can provide the ability to adaptively counter the detected FDIAs.…”

Section: Fuzzy-based Adaptive Countering Methodsmentioning

confidence: 99%

“…However, in terms of energy saving, they all are inefficient under normal situations since such en-route verification processes involve extra communication and computation overhead [4]. Therefore, centralized solutions, such as the adaptive countering scheme (ACS) [4] and the fuzzy-based FDIAs detection (FFD) [6], have been recently proposed to minimize the energy consumption due to the verification processes. They detect FDIAs through the analysis of reports collected at BSs, so that forwarding nodes need not to verify the legitimacy of MACs.…”

Section: False Data Injection Attacks and Countermeasuresmentioning

confidence: 99%

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Fuzzy-Based Adaptive Countering Method against False Data Injection Attacks in Wireless Sensor Networks

Lee

2015

IEICE Trans. Inf. & Syst.

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SUMMARYThis letter presents a method to adaptively counter false data injection attacks (FDIAs) in wireless sensor networks, in which a fuzzy rule-based system detects FDIAs and chooses the most appropriate countermeasures. The method does not require en-route verification processes and manual parameter settings. The effectiveness of the method is shown with simulation results. key words: wireless sensor networks, network security, intrusion detection, false data injection attack, fuzzy logic

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Section: Discussionmentioning

confidence: 99%

Section: Fuzzy-based Adaptive Countering Methodsmentioning

confidence: 99%

Section: False Data Injection Attacks and Countermeasuresmentioning

confidence: 99%

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Fuzzy-Based Adaptive Countering Method against False Data Injection Attacks in Wireless Sensor Networks

Lee

2015

IEICE Trans. Inf. & Syst.

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“…The vast majority of the research focusing FDIA has been conducted in the smart-grid and WSN field. In particular within power-grid state estimation, mainly for FDIA filtering and detection [Mo et al, 2010, Manandhar et al, 2014, Lee et al, 2010.…”

Section: False Data Injection Attackmentioning

confidence: 99%

An Approach for Testing False Data Injection Attack on Data Dependent Industrial Devices

Briland¹,

Bouquet²

2021

jucs

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False data injection is an attack in which an attacker injects fabricated data into a system with the objective to change the behaviour and the decision-making of the system. Many industrial data-based devices are vulnerable to such attacks, this work presents an approach for testing False Data Injection Attack. This approach uses a Domain-Specific Language to generate altered data with two objectives, to provide sophisticated attacks scenarios to increase the resilience of vulnerable systems against False Data Injection Attack and to train detection tools.

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“…Fortunately, major research efforts [2,4,5,[7][8][9][10][11] have been made to alleviate the severe damage caused by the former type of security attack, which is called a false data injection attack (FDIA). However, relatively less research [6,[12][13][14][15] has focused on investigations of countermeasures to combat the latter type of security attack, which is called a false endorsement insertion attack (FEIA).…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-Based Adaptive Countering Method against False Endorsement Insertion Attacks in Wireless Sensor Networks

Lee

2015

International Journal of Distributed Sensor Networks

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Wireless sensor networks (WSNs) are vulnerable to false endorsement insertion attacks (FEIAs), where a malicious adversary intentionally inserts incorrect endorsements into legitimate sensing reports in order to block notifications of real events. A centralized solution can detect and adaptively counter FEIAs while conserving the energy of the forwarding nodes because it does not make the nodes verify reports using cryptographic operations. However, to apply this solution to a WSN, the users must carefully select 10 or more security parameters, which are used to determine the occurrences of FEIAs. Thus, an inappropriate choice of a single parameter might result in the misinterpretation of or misdetection of FEIAs. Therefore, the present study proposes a fuzzybased centralized method for detecting and adaptively countering FEIAs in dense WSNs, where two fuzzy rule-based systems are used to detect an FEIA and to select the most effective countermeasure against the FEIA. A major benefit of the proposed method is that the fuzzy systems can be optimized automatically by combining a genetic algorithm and a simulation. Thus, users only need to write a model of the WSN to apply the proposed method to a WSN. The improved performance with this method is demonstrated by simulation results.

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Fuzzy-Based Detection of Injected False Data in Wireless Sensor Networks

Cited by 5 publications

References 11 publications

Fuzzy-Based Adaptive Countering Method against False Data Injection Attacks in Wireless Sensor Networks

Fuzzy-Based Adaptive Countering Method against False Data Injection Attacks in Wireless Sensor Networks

An Approach for Testing False Data Injection Attack on Data Dependent Industrial Devices

Fuzzy-Based Adaptive Countering Method against False Endorsement Insertion Attacks in Wireless Sensor Networks

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